1252 Site Reliability Engineer Python jobs in Dallas,TX

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Manager, Enterprise Business Development

Spectrum - Coppell, TX

Manager, Enterprise Business Development

~ 31 min Onsite

New, Posted 2 hours ago

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Principal Engineer - Python API Development

Fidelity Investments

Hurst, TX 76054

~ 31 min OnsiteEducation AssistanceHealth InsurancePaid Time OffRetirement Benefit

Bachelor's or Master's degree in Computer Science, Software Engineering, or a closely related engineering discipline
8+ years (typically 10+) building and operating production platforms and services at scale
Deep software engineering expertise in Python and distributed systems
A track record of building production‑grade services, libraries, and internal platforms
Linux fluency and scripting are required
Cloud platform leadership (AWS) —hands-on with S3, Lambda, Batch, Step Functions, EventBridge, CloudWatch, and SNS/SQS—and experience shaping platform patterns that other teams adopt
Experience enabling managed ML services (e.g., SageMaker) as part of broader platform capabilities; exposure to Azure or GCP is beneficial
DevOps and CI/CD at scale, owning standards for automated build/test/deploy (e.g., Jenkins, Git‑based workflows), containerization (Docker), release governance, and multi‑environment promotion for ML‑enabled workloads
Infrastructure as Code (CloudFormation, Terraform/OpenTofu) and platform reliability engineering (SLOs/error budgets, capacity planning, cost observability, incident response, and post‑mortems) for ML serving and data/feature pipelines
ML enablement in production: model packaging, deployment strategies (batch/online/streaming), inference routing, traffic management, performance tuning, observability, and controls for responsible use—without a research or modeling focus
Cross‑org technical leadership: you mentor junior and senior engineers, are a backbone of code review across repos, and routinely consider impacts on upstream/downstream systems when proposing changes
Set platform strategy and standards for ML packaging, deployment, serving, and observability—driving consistent adoption across squads and business units
Partner with Data Scientists to package, scale, and operationalize models; define the APIs, guardrails, and automation that take work from experimentation to reliable production
Enable secure, scalable access to traditional and generative models by collaborating with platform and application engineers to integrate through enterprise gateways and services
Advance model/data observability—tooling for data and feature drift detection, prediction‑quality monitoring and uncertainty signals, and automated diagnostics/ explainability
Lead cross‑platform incident response and post‑mortems, drive systemic fixes, and evolve standards to prevent recurrence—across applications and the platform
Uplevel engineering velocity by introducing reusable frameworks, paved paths, and CI/CD templates that simplify integration, reduce toil, and improve reliability at scale
Reduce cost and complexity across the ML ecosystem through pragmatic technology choices, clear abstractions, and a long‑term platform roadmap
The base salary range for this position is $107,000-216,000 USD per year.
Base salary is only part of the total compensation package. Depending on the position and eligibility requirements, the offer package may also include bonus or other variable compensation.

SmartExplore AI is experimental.

New, Posted 1 hour ago

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DiversityJobs

Lead Software Engineer (Python, AI) - AskGartner

Gartner

Irving, TX 75039

New, Posted 2 hours ago

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Principal Engineer - Python API Development

Fidelity Investments

Coppell, TX 75019

~ 31 min OnsiteEducation AssistanceHealth InsurancePaid Time OffRetirement Benefit

Bachelor's or Master's degree in Computer Science, Software Engineering, or a closely related engineering discipline
8+ years (typically 10+) building and operating production platforms and services at scale
Deep software engineering expertise in Python and distributed systems
A track record of building production‑grade services, libraries, and internal platforms
Linux fluency and scripting are required
Cloud platform leadership (AWS) —hands-on with S3, Lambda, Batch, Step Functions, EventBridge, CloudWatch, and SNS/SQS—and experience shaping platform patterns that other teams adopt
Experience enabling managed ML services (e.g., SageMaker) as part of broader platform capabilities; exposure to Azure or GCP is beneficial
DevOps and CI/CD at scale, owning standards for automated build/test/deploy (e.g., Jenkins, Git‑based workflows), containerization (Docker), release governance, and multi‑environment promotion for ML‑enabled workloads
Infrastructure as Code (CloudFormation, Terraform/OpenTofu) and platform reliability engineering (SLOs/error budgets, capacity planning, cost observability, incident response, and post‑mortems) for ML serving and data/feature pipelines
ML enablement in production: model packaging, deployment strategies (batch/online/streaming), inference routing, traffic management, performance tuning, observability, and controls for responsible use—without a research or modeling focus
Cross‑org technical leadership: you mentor junior and senior engineers, are a backbone of code review across repos, and routinely consider impacts on upstream/downstream systems when proposing changes
Set platform strategy and standards for ML packaging, deployment, serving, and observability—driving consistent adoption across squads and business units
Partner with Data Scientists to package, scale, and operationalize models; define the APIs, guardrails, and automation that take work from experimentation to reliable production
Enable secure, scalable access to traditional and generative models by collaborating with platform and application engineers to integrate through enterprise gateways and services
Advance model/data observability—tooling for data and feature drift detection, prediction‑quality monitoring and uncertainty signals, and automated diagnostics/ explainability
Lead cross‑platform incident response and post‑mortems, drive systemic fixes, and evolve standards to prevent recurrence—across applications and the platform
Uplevel engineering velocity by introducing reusable frameworks, paved paths, and CI/CD templates that simplify integration, reduce toil, and improve reliability at scale
Reduce cost and complexity across the ML ecosystem through pragmatic technology choices, clear abstractions, and a long‑term platform roadmap
The base salary range for this position is $107,000-216,000 USD per year.
Base salary is only part of the total compensation package. Depending on the position and eligibility requirements, the offer package may also include bonus or other variable compensation.

SmartExplore AI is experimental.

New, Posted 1 hour ago

Recommended

DiversityJobs

Site Reliability Engineer

Optimum Communications

Plano, TX 75086

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Reliability Foreman

Ecolab

Garland, TX 75040

~ 26 min OnsiteChildcare AssistanceHealth InsurancePaid Time OffRetirement Benefit

High school diploma or GED
5 years of maintenance experience in an industrial, commercial, or manufacturing environment
Demonstrated experience leading or overseeing maintenance staff
Working knowledge of mechanical, electrical, and building systems
Ability to read blueprints, schematics, and technical manuals
Experience with maintenance management systems (CMMS) or work order tracking
Experience with building construction coordination
Immigration sponsorship is not available for this role

SmartExplore AI is experimental.

New, Posted 1 hour ago

Recommended

DiversityJobs

Lead Software Engineer (Python, Invoice/BIlling)

Gartner

Irving, TX 75039

New, Posted just now

ZipRecruiter

Network Engineer_One-of-Multi_US_Locations_Onsite

Wired360

Dallas, TX 75217

New, Posted 1 hour ago

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Apprentice Plumber

Upchurch

Arlington, TX 76010

~ 36 min OnsiteUrgently HiringHealth InsurancePaid Time Off

Current Texas Plumbing Apprentice License required.
All applicants must possess a current Texas Plumbing Apprentice License at the time of application.
Basic knowledge of hand tools and power tools.
Reliable transportation to travel to various job sites as needed.
Ability to pass pre-employment screening and maintain job site access requirements.
Ability to lift and carry materials weighing up to 50 pounds or more with assistance when needed.
Ability to stand, walk, bend, kneel, crouch, and climb ladders for extended periods.
Ability to work in indoor and outdoor environments and varying weather conditions.
Comfortable working at heights, in confined spaces, and on active construction sites.
Ability to use hand and power tools safely and effectively.

SmartExplore AI is experimental.

New, Posted 1 day ago

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